AI is shaking things up in the traditional finance and fintech industry for good. Even if you have never heard of the capabilities of AI to detect frauds and offer more enhanced customer service and overall customer experience, you will have interacted with a chatbot a.k.a. personal assistant on your bank’s website, probably.
According to Accenture’s Redefine Banking with Artificial Intelligence report, the banks that will derive the most value out of AI will be those ready to rethink their strategies on people, processes, and data.
Let’s explore how banks can make use of AI to constantly innovate at scale and derive optimal value.
Customer Support through Conversational AI
Customers of financial services can’t take any more of their legacy support efforts. We need smart and intelligent bots assisting us on our way through the banking journey. Kasisto is making conversational AI possible and feasible for banks. The company’s major contribution has been its AI platform KAI, which banks use to build their own virtual assistants.
KAI’s rooted in natural language processing and AI reasoning, which makes the chatbot capable of handling sophisticated questions about finance management- a rare capability considering all leading banking chatbots today.
Read More: Why Banks will Benefit from Open API
Wealth Management Services Powered by AI
One of the areas in banking that has recently witnessed considerable investment is wealth management with AI. Industry heavyweights are now acquiring technology start-ups with a focus on automated analysis of massive unstructured data.
The purpose of this data is to predict ‘typical’ behavioral patterns. Experts now hope to build AI engines to offer them insights on how best to serve high net-worth clients. By automating wealth management for these clients, banks would be able to offer personalized, tax-optimized investment suggestions to their clients with comparably less investable assets than needed to qualify for professional wealth management.
Personalized Financial Services
Customer expectations from their banks have evolved over the years. They now expect personalized services at competitive costs. Traditional automation stands at the risk of degrading over time. But intelligent automation gets stronger and smarter by itself.
AI can make personalization happen for banks. Allowing them to offer personalized products to customers based on their previous transactions, hinted interests, past searches, and more. BCG estimates that banks can garner as much as $300 million in revenue growth for every $100 billion they have in assets by personalizing their customer interactions.
We know this could have been included in the Conversational AI tab. We think it deserves to be mentioned separately. Voice assisted banking is gradually moving to the mainstream as customers interact with voice commands rather than a touch screen.
The natural language technology, an arm of AI, can help process voice queries to answer questions, find information, and connect customers with various banking services. More recently, the Swiss bank UBS partnered with the tech behemoth Amazon to integrate its “Ask UBS” service with Amazon Echo.
Securing Digital Banking with AI
Banks have access to a large amount of sensitive data. As a majority of customers choose to transact online, banks are expected to look for ways to secure these transactions. Artificial intelligence is now helping banks to just that.
AI systems can be used to safeguard customer information against malware, phishing, ransomware, and so on. This can be achieved effectively with cognitive fraud analytics, customer behavior monitoring, and real-time pattern profiling. As AI-based models look at customer behavior patterns instead of a specific set of rules, AI systems are more likely to identify fraud than manual monitoring efforts.
CitiBank has partnered with Feedzai, a global data science company, to identify and eliminate fraud in real-time using artificial intelligence.
Automated Loans Processing and Credit Scoring
AI is not only useful in automating menial and monotonous tasks. These systems can also be trained to make business decisions that would normally need cognitive thinking.
Banks and credit scorers can use ML models to track a customer’s credit history to make informed decisions on loan approvals. Such a model can score borrowers by their creditworthiness by factoring in massive data pertaining to financial, social media, and internet activity.
An AI-based model can make credit scoring painless, secure, and credible as a process.
AI for Better Talent Management
Fintech firms and banks might soon rely on artificial intelligence to close the talent gap as well as manage their resources better. Finding, hiring, and managing talent continues to be a challenge for banks across functions such as marketing, sales, development, engineering, customer support, and so on.
Banks will soon look for better ways to manage talent acquisition and retention. Alongside training existing employees to increase organizational skill fluency with AI. According to the Talent Intelligence and Management Report 2019-2020 by Harris Interactive in collaboration with Eightfold, 73 percent of US CHROs and CEOs will use more AI in the next three years to improve talent management.
Artificial intelligence is poised to disrupt the banking landscape in more ways than one. How do you think AI will influence how fintech apps are built?